Being a detection model, the proposed network can also perform traditional ZSR. We evaluate ZSR performance on popular Caltech-UCSD Birds-200-2011 (CUB) dataset [1]}. This dataset contains 11,788 images from 200 classes and provides single bounding boxes per image. Following standard train/test split [2]}, we use 150 seen and 50 unseen classes for experiments. For semantics embedding, we use 400-d word2vec (w2v) and GloVe (glo) vector [3]}. Note that, we do not use per image part annotation (like [4]}) and descriptions (like [5]}) to enrich semantic embedding. For a given test image, our network predicts unseen class bounding boxes. We pick only one label with the highest prediction score per image. In this way, we report the mean Top1 accuracy of all unseen classes in Table REF . One can find our proposed solution achieve significant performance improvement in comparison with state-of-the-art methods.
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